ArticleJournal of proteome research2018
Comparison of Quantitative Mass Spectrometry Platforms for Monitoring Kinase ATP Probe Uptake in Lung Cancer.
Article in Journal of proteome research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Cross-platform clinical proteomics using the Charité open standard for plasma proteomics (OSPP).Nature communications · 2025Article
- In-depth analysis of data characteristics and comparative evaluation of dda and dia accuracy in label-free quantitative proteomics of biological samples.Clinical proteomics · 2025Article
- Quantification of Peptides in Food Hydrolysate fromFoods (Basel, Switzerland) · 2025Article
- Concatemer-assisted stoichiometry analysis: targeted mass spectrometry for protein quantification.Life science alliance · 2025Article
- Concatemer Assisted Stoichiometry Analysis (CASA): targeted mass spectrometry for protein quantification.bioRxiv : the preprint server for biology · 2024Article
- DecipheringmSystems · 2024Article
- Review
- Deep Learning Based MS2 Feature Detection for Data-Independent Shotgun Proteomics.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
- PRM-LIVE with Trapped Ion Mobility Spectrometry and Its Application in Selectivity Profiling of Kinase Inhibitors.Analytical chemistry · 2021Article
- GLOBAL AND TARGETED PROFILING OF GTP-BINDING PROTEINS IN BIOLOGICAL SAMPLES BY MASS SPECTROMETRY.Mass spectrometry reviews · 2021Review
- Targeted Protein Quantification Using Parallel Reaction Monitoring (PRM).Methods in molecular biology (Clifton, N.J.) · 2021Article
- Driving Under the Influence of Drugs: A Single Parallel Monitoring-Based Quantification Approach on Whole Blood.Frontiers in chemistry · 2020Article
- Determining Allele-Specific Protein Expression (ASPE) Using a Novel Quantitative Concatamer Based Proteomics Method.Journal of proteome research · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Recent developments in instrumentation and bioinformatics have led to new quantitative mass spectrometry platforms including LC-MS/MS with data-independent acquisition (DIA) and targeted analysis using parallel reaction monitoring mass spectrometry (LC-PRM), which provide alternatives to well-established methods, such as LC-MS/MS with data-dependent acquisition (DDA) and targeted analysis using multiple reaction monitoring mass spectrometry (LC-MRM). These tools have been used to identify signaling perturbations in lung cancers and other malignancies, supporting the development of effective kinase inhibitors and, more recently, providing insights into therapeutic resistance mechanisms and drug repurposing opportunities. However, detection of kinases in biological matrices can be challenging; therefore, activity-based protein profiling enrichment of ATP-utilizing proteins was selected as a test case for exploring the limits of detection of low-abundance analytes in complex biological samples. To examine the impact of different MS acquisition platforms, quantification of kinase ATP uptake following kinase inhibitor treatment was analyzed by four different methods: LC-MS/MS with DDA and DIA, LC-MRM, and LC-PRM. For discovery data sets, DIA increased the number of identified kinases by 21% and reduced missingness when compared with DDA. In this context, MRM and PRM were most effective at identifying global kinome responses to inhibitor treatment, highlighting the value of a priori target identification and manual evaluation of quantitative proteomics data sets. We compare results for a selected set of desthiobiotinylated peptides from PRM, MRM, and DIA and identify considerations for selecting a quantification method and postprocessing steps that should be used for each data acquisition strategy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.